


"""simple docstring"""






import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


_lowerCAmelCase							:    str					   =	re.compile(R'''\s+''')







def        lowerCamelCase_(		_lowerCamelCase       )    ->     int:
   '''simple docstring'''





   return {"hash": hashlib.mda(re.sub(_lowerCamelCase			,		""			,		example["content"]       ).encode("utf-8"       )       ).hexdigest()}







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     [len(_lowerCamelCase       ) for line in example["content"].splitlines()]
   return {"line_mean": np.mean(_lowerCamelCase       ), "line_max": max(_lowerCamelCase       )}







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[Any]							=     np.mean([c.isalnum() for c in example["content"]]       )
   return {"alpha_frac": alpha_frac}







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Tuple:
   '''simple docstring'''





   if example["hash"] in uniques:
      uniques.remove(example["hash"]       )
      return True
   else:
      return False







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=5       )    ->     Optional[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				str							=     ["auto-generated", "autogenerated", "automatically generated"]
   _lowerCamelCase   :				str							=     example["content"].splitlines()
   for _, line in zip(range(_lowerCamelCase       )			,		_lowerCamelCase       ):
      for keyword in keywords:
         if keyword in line.lower():
            return {"autogenerated": True}
   else:
      return {"autogenerated": False}







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=5			,		_lowerCamelCase=0.0_5       )    ->     Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Tuple							=     ["unit tests", "test file", "configuration file"]
   _lowerCamelCase   :				List[Any]							=     example["content"].splitlines()
   _lowerCamelCase   :				List[str]							=     0
   _lowerCamelCase   :				Optional[Any]							=     0
   # first test
   for _, line in zip(range(_lowerCamelCase       )			,		_lowerCamelCase       ):
      for keyword in keywords:
         if keyword in line.lower():
            return {"config_or_test": True}
    # second test
   _lowerCamelCase   :				Dict							=     example["content"].count("\n"       )
   _lowerCamelCase   :				Optional[Any]							=     int(coeff * nlines       )
   for line in lines:
      count_config += line.lower().count("config"       )
      count_test += line.lower().count("test"       )
      if count_config > threshold or count_test > threshold:
         return {"config_or_test": True}
   return {"config_or_test": False}







def        lowerCamelCase_(		_lowerCamelCase       )    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     ["def ", "class ", "for ", "while "]
   _lowerCamelCase   :				Union[str, Any]							=     example["content"].splitlines()
   for line in lines:
      for keyword in keywords:
         if keyword in line.lower():
            return {"has_no_keywords": False}
   return {"has_no_keywords": True}







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=4       )    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     example["content"].splitlines()
   _lowerCamelCase   :				List[Any]							=     0
   for line in lines:
      counter += line.lower().count("="       )
      if counter > minimum:
         return {"has_few_assignments": False}
   return {"has_few_assignments": True}







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Optional[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				List[str]							=     tokenizer(example["content"]			,		truncation=_lowerCamelCase       )["input_ids"]
   _lowerCamelCase   :				Tuple							=     len(example["content"]       ) / len(_lowerCamelCase       )
   return {"ratio": ratio}







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				str							=     {}
   results.update(get_hash(_lowerCamelCase       )       )
   results.update(line_stats(_lowerCamelCase       )       )
   results.update(alpha_stats(_lowerCamelCase       )       )
   results.update(char_token_ratio(_lowerCamelCase       )       )
   results.update(is_autogenerated(_lowerCamelCase       )       )
   results.update(is_config_or_test(_lowerCamelCase       )       )
   results.update(has_no_keywords(_lowerCamelCase       )       )
   results.update(has_few_assignments(_lowerCamelCase       )       )
   return results







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     str:
   '''simple docstring'''





   if not check_uniques(_lowerCamelCase			,		_lowerCamelCase       ):
      return False
   elif example["autogenerated"]:
      return False
   elif example["line_max"] > args.line_max:
      return False
   elif example["line_mean"] > args.line_mean:
      return False
   elif example["alpha_frac"] < args.alpha_frac:
      return False
   elif example["ratio"] < args.min_token_ratio:
      return False
   elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
      return False
   elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
      return False
   elif example["has_few_assignments"]:
      return False
   else:
      return True







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





   with open(_lowerCamelCase			,		"rb"       ) as f_in:
      with gzip.open(str(_lowerCamelCase       ) + ".gz"			,		"wb"			,		compresslevel=6       ) as f_out:
         shutil.copyfileobj(_lowerCamelCase			,		_lowerCamelCase       )
   os.unlink(_lowerCamelCase       )


# Settings
_lowerCAmelCase							:    Dict					   =	HfArgumentParser(PreprocessingArguments)
_lowerCAmelCase							:    List[str]					   =	parser.parse_args()
if args.num_workers is None:
  _lowerCAmelCase							:    Optional[Any]					   =	multiprocessing.cpu_count()
_lowerCAmelCase							:    Any					   =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
_lowerCAmelCase							:    Union[str, Any]					   =	time.time()
_lowerCAmelCase							:    str					   =	load_dataset(args.dataset_name, split='''train''')
print(f'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
_lowerCAmelCase							:    List[Any]					   =	time.time()
_lowerCAmelCase							:    Optional[int]					   =	ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
_lowerCAmelCase							:    Dict					   =	set(ds.unique('''hash'''))
_lowerCAmelCase							:    Union[str, Any]					   =	len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
_lowerCAmelCase							:    Tuple					   =	time.time()
_lowerCAmelCase							:    List[Any]					   =	ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(f'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(f'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
  _lowerCAmelCase							:    Union[str, Any]					   =	time.time()
  _lowerCAmelCase   ,  _lowerCAmelCase							:    Optional[int]					   =	deduplicate_dataset(ds_filter, args.jaccard_threshold)
  print(f'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
  print(f'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
_lowerCAmelCase							:    List[str]					   =	Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
  with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
    json.dump(duplicate_clusters, f)

_lowerCAmelCase							:    Optional[int]					   =	output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

_lowerCAmelCase							:    Optional[int]					   =	time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
  _lowerCAmelCase							:    Tuple					   =	str(data_dir / f'''file-{file_number+1:012}.json''')
  _lowerCAmelCase							:    Optional[Any]					   =	min(len(ds_filter), index + args.samples_per_file)
  ds_filter.select(list(range(index, end_index))).to_json(file_path)
  compress_file(file_path)

print(f'''Time to save dataset: {time.time()-t_start:.2f}''')

"""simple docstring"""



import math




class 		a_    :







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							list[list[float]]  ,       __UpperCamelCase      :							list[int]  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = 0.0
        _UpperCAmelCase        = 0.0
        for i in range(len(__UpperCamelCase  )  ):
            da += math.pow((sample[i] - weights[0][i])  ,       2  )
            da += math.pow((sample[i] - weights[1][i])  ,       2  )
            return 0 if da > da else 1
        return 0







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							list[list[int | float]]  ,       __UpperCamelCase      :							list[int]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							float  )		->list[list[int | float]]:






        '''simple docstring'''






        for i in range(len(__UpperCamelCase  )  ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights







def 			_UpperCamelCase						(    )     ->	None:





    """simple docstring"""

    _UpperCAmelCase        = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    _UpperCAmelCase        = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    _UpperCAmelCase        = SelfOrganizingMap()
    _UpperCAmelCase        = 3
    _UpperCAmelCase        = 0.5

    for _ in range(_A   ):
        for j in range(len(_A   )   ):
            # training sample
            _UpperCAmelCase        = training_samples[j]

            # Compute the winning vector
            _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

            # Update the winning vector
            _UpperCAmelCase        = self_organizing_map.update(_A       ,   _A       ,   _A       ,   _A   )

    # classify test sample
    _UpperCAmelCase        = [0, 0, 0, 1]
    _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

    # results
    print(F"""Clusters that the test sample belongs to : {winner}"""   )
    print(F"""Weights that have been trained : {weights}"""   )


# running the main() function
if __name__ == "__main__":
 main()